Archive for Strategy

In today’s game, reliever usage may be more important than ever. As starters go less deep into games, more emphasis is placed on bullpen strategy to survive the mid-to-late innings. Teams can use data to streamline this process, strategizing relief pitcher usage based on their pitch repertoires and batter ability. My goal is to produce a matchup tool that can potentially give us some insight as to how the big league teams “play the matchups.”

The basis of a bullpen matchup recommender will be at the pitch level: what types of pitches does a particular hitter struggle against, and how do they align with what a particular pitcher throws? To do this, I will first use clustering methods in order to redefine pitcher arsenals based on pitch flight characteristics. Matchups will then be selected according to which pitcher is expected to perform the best against a given batter, optimizing pitcher strengths against batter weaknesses.

Data

To conduct this research I used available Statcast data from 2016-2021 (through this year’s trade deadline). My variables of interest are as follows: pitch location (plate_x & plate_z), perceived pitch speed derived from release extension (effective_speed), pitch movement (pfx_x & pfx_z), spin rate (release_spin_rate), and the newly introduced spin axis (spin_axis). I elected to include spin axis in order to account for how the batter may see the pitch as it’s released. All in all, the variables selected measure the stuff and location of each pitch so that we may classify them more accurately beyond the basic pitch type labels. After cleaning this dataset and removing outliers, I was ready to move on to the modeling process. Read the rest of this entry »

Our beloved pastime has a long history of over-the-hill veteran players serving important mentor roles around the game, but the primacy of the Competitive Balance Tax and the perpetual crush of roster spot competition and “efficiency” has rendered these players largely moot. Players like the 40-something version of Jason Giambi, a bench bat for years on the strength of his contributions to his team as a leader beyond just his metric value, have grown frightfully rare. It is sad to see that sort of quasi-player/coach fade to memory.

As I look over the U.S. Olympic Roster, I see an awful lot of well-loved veterans who have lost a step over the years and, with that lost step, any serious hope of a consistent job under the new normal of roster construction. But I am convinced there remains value to the game of baseball to have players like Todd Frazier, Edwin Jackson, Scott Kazmir, and David Robertson around the sport beyond what they contribute to the back of the baseball card. A glance at the current free agent list reveals a small glut of other interesting, memorable players, such as Matt Kemp, Ryan Braun, Matt Wieters, and Neil Walker, to name a few. Read the rest of this entry »

This post was inspired by a couple of articles that I remembered reading from Jonah Pemstein back in 2014. The intention of those posts was to predict the result of any given batter/pitcher matchup, dubbed the “Outcome Machine.” Have you ever wondered what the probability Mike Trout strikes out when he steps into the box against Justin Verlander? Of course, there are variables that are specific to any plate appearance (umpires/situation/stadium/etc.) that are harder to quantify, but it set out to predict the outcome in a vacuum. Trout vs. Verlander and nothing else (For the record, in 2020, I would estimate the answer is about 27.5%).

Being able to predict the outcomes in sports would take most of the fun out of being a spectator, sure, but I still found myself coming back to those articles. While reading and re-reading in an attempt to understand the logic and fool around with the equations, I came to a few questions of my own:

• With all of the hubbub of juiced balls and increased launch angles, do equations that were based on data from 2003-13 still apply to the game today?
• The regression equations were composed of the at-bat result and the stats of the batter and pitcher from the same year. This stuck out to me as an issue because it means the player’s performance later in the season, say in July, influences the prediction of an at-bat in May, and to a lesser extent, the result of that specific at-bat is already baked into that season’s performance. Shouldn’t you use data exclusively before a given at-bat to predict the outcome? Hindsight is 20/20, after all.

Eventually curiosity got the best of me and I decided to emulate the original exercise. Before I really start to nerd out on the inner workings, you can find this iteration of the Outcome Machine as a Google Sheet here. You can either select a pitcher/batter combination through the dropdown or hard key in the rates in a custom, hypothetical matchup below that. League average is set by default to projections for 2020 but can be updated as desired in the custom matchup. I would note that the preset statistics in this tool are total projections for 2020 but not broken out into L/R splits, as to my knowledge that data is currently behind a paywall. Read the rest of this entry »

The concept of “the shift” has become more widely used throughout major-league baseball. While some teams shift more than most, others are shifted against more than most. The Shift Era is still relatively new as teams dive deeper and deeper into the analytical realm to increase winning percentage. However, is using the shift actually effective?

I believe that there are certainly situations where the shift should be utilized. Players such as David Ortiz, Albert Pujols, Brian McCann, etc. generally are the style of players to shift against. Older players generally rely more on pulling the ball because they are able to generate more power. These styles of pull-only hitters are usually prime targets for shifting against. My question is, why haven’t these players adapted their swing against the shift?

When learning swing mechanics, you’re taught to square up the baseball and drive the ball where it’s pitched. When shifting, pitchers are forced to make very selective pitches to avoid batters driving the ball the other way through the shift. This is hard for pitchers because it takes away some of their effectiveness. Hitters are beginning to find ways to beat the shift and steal easy hits. If a batter is in a shift situation, they can essentially eliminate pitches towards the outside half of the plate. Knowing the pitcher’s pitch arsenal, the batter can then be selective in his approach. Depending on the count, the batter can determine the next pitch, whether it’s offspeed or a fastball. Obviously a tailing fastball in on the hands is hard not to roll over into the shift, but that’s just good pitching.

Batters are finally beginning to grasp that they can beat the shift by simply putting down a bunt down the line. Or, they can create longer bat lag from their hands letting the ball travel deeper in the zone and taking the ball to the opposite field. The best hitters in baseball are those who can hit to all areas of the field. Charlie Blackmon was shifted against 121 times this year; he hit .412 against the shift. Why in the world would teams shift against him 121 times? Kris Bryant was shifted against 210 times; he hit .364. Players like this who are able to adapt their swing progressions at the plate should not be shifted against this often. Teams are simply giving them easy hits, which lead to runs. The whole point of the shift is to avoid baserunners, right?

Again, there are some batters against whom shifting works. Brian McCann was shifted against 248 times and still hit .243 against the shift, which is still pretty good considering it’s towards the bottom of the league. Lucas Duda was shifted against 241 times, hitting .243; still not terrible. Again, there are situations you can get away with shifting. The only time teams should shift should be with no runners on, strict pull hitters, and with a pitcher who’s comfortable with pitching inside.

When teams shift with runners on, I believe it’s a terrible strategy. It’s considerably difficult turning a routine double play with players out of their positions. Also, it’s difficult to catch runners stealing when you have a third baseman trying to find the bag and make the tag. Players like Dustin Pedroia have taken advantage of teams using the shift with runners on to take the extra base with the third baseman out of position. Players are beginning to find holes in the shift and are taking advantage, leading to runs.

When shifting, I believe the best option is to leave the shortstop between 2^nd and 3^rd, the second baseman shaded up the middle towards the bag, and the third baseman moving into right field between 1^st and 2^nd. With the third baseman in this position, he can create the same angle to 1^st as when he’s at 3^rd. This way players are in more comfortable standard positions, keeping the double play a more viable option. Shifting works in certain situations, but teams need to be more careful as hitters begin to adapt their approaches and steal easy hits, using the shift against the enemy.

How many teams are looking for a cheap starting pitcher to be a veteran presence for a young rotation? Looking for an upgrade over what they currently have for starting pitching? Or just need a warm body to fill the hole left by Joe Ross with someone not named Edwin Jackson? As far as I can tell, 10 teams are looking for a 3/4 starter such as Merrill Kelly, especially considering his stats that he has accumulated in this season (maybe he’ll get one more start to add to his excellent season so far) have been particularly impressive. All this when the Rays thought that Merrill Kelly was just a “AAA starter” who could be a bullpen guy in the big leagues.

Merrill Kelly in the minor leagues was a solid minor leaguer who would become a swingman with the Durham Bulls. In his age-25 season, he went 9-4 in 114 IP with a 2.76 ERA, a 3.74 FIP, and a 3.57 xFIP. Which looked good with his 8.53 K/9 and 2.92 BB/9, a .298 BABIP, and a 47.9% ground-ball rate as well. Perhaps he could a solid swingman/fifth starter in the big leagues. The Rays apparently thought otherwise and said either he’d be a bullpen pitcher for the MLB team or a starter in AAA. Merrill Kelly thought otherwise and went to South Korea to play for the SK Wyverns.

Merrill Kelly in South Korea was all right in his first season, with an 11-10 record in 30 games (29 starts), 181 IP, and an ERA of 4.13. With peripheral rates that weren’t as good (6.91 K/9 and 2.69 BB/9). His next season was similar, with a 9-8 record in 31 games, but a great 200 1/3 IP with similar rate stats: 3.68 ERA, 6.83 K/9, and 2.70 BB/9. This year has been very different for him, with a 15-7 record in 29 games and 185 IP with a 3.65 ERA; his rate stats are much more improved, at 8.90 K/9 and 2.14 BB/9.

What is he doing differently to get these improved stats? Why is his ERA as high as it is, despite getting more strikeouts and walking fewer batters? He is allowing more pesky little hits: that is, his defense is not getting as many outs made as it should (1.08 hits per inning this year, vs 1.03 hits per inning in 2015-2016 combined). He has also allowed one more homer and two more doubles than last year, in 15 1/3 fewer IP.

His repertoire:

-4 Seam Fastball – 92-94 MPH (back in 2015, he was throwing 88-91 MPH)

-2 Seam Fastball – couple of miles slower and has slight sink, and runs in an opposite direction. He mixes this pitch well with his fastball

-Cutter – He started to throw this pitch more once he got to Korea and has mixed it well with his other fastballs and change

-Slider – Has a good slider that can break sharply when he’s pitching well. About 83-87 MPH

-Curveball – Decent enough curve but probably not his best pitch. Up 78-80 MPH

-Circle Changeup – Good sinking and running movement. He throws it about 85 MPH. One of his top pitches

What has he improved? Velocity on his pitches, sharper movement to his fastballs and changeup, getting better with the cutter, and improving his control. (This quoted from this article on Reddit: Merrill Kelly scouting report and info, which I think explains his improvements, but I disagree with his assessment of Merrill Kelly’s talent.) Given the talent level of the average hitter in the KBO is around AAA level, he should be able to perform as around a low-3/high-4 starter, as I’d say he is better than the average starter. A funny thing of note is that the Rays have another version of Merrill Kelly named Ryan Yarbrough, who has pitched better than Kelly did at a similar age; hopefully they’ll give him a chance to prove the Rays wrong for letting Merrill Kelly go.

Since he is on the right side of 30 and will pitch the 2018 season at age 29, I’d offer him a three-year deal worth $6 million per year with incentives that could boost the value of the deal to around $24 million over three years, with an option for a fourth season at $7 million (buyout of $2 million) with incentives to boost the option value to $10 million. This is due to his risk, and likely lower than what Phil Hughes was offered after the 2013 season from the Twins.

Who are the 10 teams that could use Merrill Kelly as a starting pitcher? The answers might be more surprising at first glance than other answers. The best choice would be the Miami Marlins for the same reasons listed, but it could become a wild-card contender taking a chance for Kelly to make more money in a playoff cut. The second-best choice is one that is pretty questionable, depending on whether the Nationals are willing to take a risk on a player from the KBO and whether they want someone better than him. But he’d be great for them in place of Joe Ross, and would be an upgrade over their current options; plus he would be cheap enough to fit in their payroll. One issue is that the Nationals have a hitter-friendly park, but not having to face the Nationals would mitigate some of those concerns. The San Diego Padres would be the third-best choice due to the non-DH league, an extremely pitcher-friendly park according to MLB park factors, and multiple available rotation spots, but they are in a tough NL West and aren’t likely to be a playoff team.

The next one is questionable but they would certainly be able to make room for him — the Oakland A’s have always been unconventional, and the park is usually known for being pitcher-friendly. The Twins would be similar to the A’s in those respects and are in fact a playoff threat (I didn’t expect to be saying this about the Twins this year at all). The Royals are practically in a tie with the Twins and A’s due to a pitcher-friendly park, although their team is going to be worse due to many key players leaving (Cain, Hosmer, and Moustakas).

Despite the Rangers having a definite hole in the rotation (who would let Nick Martinez or A.J. Griffin start in an extreme hitter-friendly park?), they are the seventh-best option due to that park, the DH league, and just not having a great team in general. The White Sox are an even more extreme version of the Rangers, and are extremely bad as well; I doubt he’d want to play for such a poor team. Same with the Reds, except there is no DH, but the Reds might want to give younger options a try first. The Orioles have almost all the bad factors: A league with a DH, a hitter-friendly park, a tough division, a bad defensive team, and generally bad development staff that has done more harm than good for its pitchers.

I would love to see one of the top six teams sign Kelly to a contract, since those would be best for him getting another contract after the first one expires. Can’t wait for him to get his shot in the big leagues, to prove his previous doubters wrong, and to have a long and successful career in the MLB.

All stats are owned by their respective owners (ESPN, FanGraphs, KBO, Reddit), I own none of the stats used. All stats are as of 9-23-2017.

I went over the starters version of dScore here, so I’m not going to re-visit that here. I’ll just jump right in with the list!

Risers/Fallers

Danny Salazar (+16) – dScore never gave up on him, despite him being absolute trash early on this year. He came back and dominated, launching him up the ranks even farther in the process. Current status: injured. Again.

Sonny Gray (newly ranked) – If there were any doubts about the Gray the Yankees dealt for, he’s actually surpassed his dScore from his fantastic 2015 season. He’s legit (again).

Alex Wood (-8) – Looks like the shoulder issues took a bit of a toll on his stuff, but dScore certainly isn’t out on him.

Dallas Keuchel (-8) – Keuchel’s stuff isn’t the issue. He’s still a buy for me.

Lance McCullers (-11) – Poor Astros. Maybe not too poor though; their aces have gotten hammered but haven’t fallen far at all. McCullers is going to bounce back.

The Studs

Some light flip-flopping at the top, with Kluber taking over at #1 from Scherzer. The Klubot’s been SO unconscious. Everyone else is pretty much the usual suspects.

The Young Breakouts (re-visited)

Zack Godley (11) – He’s keeping on keeping on. He barely moved since last month’s update, and I’m all-in on him being a stud going forward.

Luis Castillo (9) – He’s certainly done nothing to minimize the hype. In fact, he’s added a purely disgusting sinker to his arsenal and it’s raising the value of everything he throws. Also, from a quick glance at the Pitchf/x leaderboards, two things stand out to me. He seems to have two pitches that line up pretty closely to two top-end pitches: his four-seamer has a near clone in Luis Severino’s, and his changeup is incredibly similar to Danny Salazar’s. That’s a nasty combo.

James Paxton (15) 

The Test Case

Buck Farmer (20) – Okay, so to be honest when he showed up on this list, I absolutely thought it was a total whiff. By ERA he’s been a waste, but he’s really living on truly elite in-zone contact management, swinging strikes, K/BB, and hard-hit minimization. His pitch profile is middling (not bad, but not great either), so I really don’t think he’s going to stay this high much longer. He’s certainly doing enough to earn this spot right now, and I’d expect him to not run a 6+ ERA for much longer.

The Loaded Teams

Yankees – Luis Severino (12), Sonny Gray (17), Masahiro Tanaka (25) / Some teams have guys higher up, but the Yankees are loaded up and down.

Astros – Dallas Keuchel (14), Lance McCullers (19), Brad Peacock (18), Charlie Morton (23) / Similar to the Yankees. Morton and Peacock are having simply phenomenal years.

The Dropouts

Rich Hill (39)

Trevor Cahill (35)

Marcus Stroman (28)

Poor Rich Hill. Lost his perfect game, then lost the game, then lost his spot in the top 25. Cahill’s regressed to #DumpsterFireTrevor since his trade to the Royals. Stroman really didn’t fall that far…and his slider is still a work of art.

The Just Missed

Jordan Montgomery (26) – Too bad the Yankees couldn’t send down Sabathia instead. This kid is good.

Aaron Nola (27) – #Ace

Carlos Martinez (29) – Martinez simply teases ace upside, but frankly I think you can pretty much lump him and Chris Archer (30) in the same group — high strikeouts, too many baserunners and sub-ace starts to move into the top tier.

Dinelson Lamet (32) – He’s absolutely got the stuff. He could stand to work on his batted-ball control though.

Jimmy Nelson (34) – dScore buys his changes. He finished at #148 last year. I’ll call him a #2/3 going forward.

Notes from Farther Down

Jose Berrios is all the way down to 47. His last month cost him 19 spots, but frankly it could be much worse: Sean Manaea lost 39 spots, down to 87. Manaea really looks lost out there. I don’t want to point at the shoulder injury he had earlier this year since his performance really didn’t drop off after that…but I’m wondering if he’s suffering from some fatigue that’s not helped by that. He’s pretty much stopped throwing his toxic backfoot slider to righties, and that’s cost him his strikeouts. Michael Wacha is another Gray-like Phoenix: he’s up to 52 on the list, once again outperforming his 2015 year. I’m cautiously buying him as a #3 with upside. And finally, buzz round: Mike Clevinger (33), Alex Meyer (36), Robbie Ray (38), Rafael Montero (41), and Jacob Faria (43) are already ranked quite highly, and outside of Montero and maybe Meyer I could see all of them bumping up even higher. Clevinger’s really only consistency away from being a legitimate stud.

My next update will be the end-of-season update, so I think I’m going to do a larger ranking than just the top 25; maybe all the way down to 100. Enjoy the last month-plus!

The Mets are the talk of the town…for all the wrong reasons. They currently sit at a 31-41 record and are 12 games behind the Washington Nationals in the NL East, which as of now seems to be theirs for the taking. The Mets boast one of the worst bullpens in the majors and have been plagued by injuries as well as underperformance from the bulk of their lineup. With the results of this season, many are beginning to wonder if it’s time to turn the page on this current pack of Mets players, many of whom were on the 2015 team that lost to the feisty Kansas City Royals in the World Series. I will attempt to go group by group in an effort to determine whether or not the Mets should begin a new rebuilding process, the most dreaded phrase in sports.

Starting with the outfield, Yoenis Cespedes is locked in for three more years in his current contract. It’s understandable why the Mets were looking to sign him in the offseason based on his performance in 2015 and 2016. However, injuries and poor performance have contributed to the current record that the Mets have. Cespedes still won’t lose his spot in left. Curtis Granderson, due to his age, will most likely not be re-signed, as well as Jay Bruce who, if he is not traded before the deadline, will most certainly test free agency. Juan Lagares has been injury-prone the last couple years but the one piece of good news is that Michael Conforto has seen a resurgence since coming back from Triple-A Las Vegas. Also, one of their top prospects, Brandon Nimmo, should receive regular playing time in the outfield, if not this season, then definitely in 2018.

Next, we have the infield, which has been decimated by injuries. Neil Walker and Asdrubal Cabrera have struggled through injuries (and who knows if/when David Wright will ever step on a baseball field again). Jose Reyes and Lucas Duda have mightily underperformed. The good news for the Mets is that Cabrera, Walker, and Reyes will be gone after the season, which means that the infield can get much younger. Top prospects Dominic Smith and Amed Rosario will be September call-ups and, if all goes well, can be regulars in the lineup next year. T.J. Rivera and Wilmer Flores have proven to be reliable pieces in the lineup. Despite some injuries from Flores, he has made up for it with his versatility in both the field and in the lineup, giving manager Terry Collins options to choose from. While Flores and Rivera may not be long-term solutions, they are the best options that the Mets have at the moment. As far as catching is concerned, Travis d’Arnaud is probably the Mets’ best option right now, although he has severely underperformed since being traded to them. The Mets should try to get another catcher in free agency.

Finally, the best pitching staff is a huge question mark, but also a big concern among scouts. Matt Harvey clearly no longer has any interest in remaining with the team and Noah Syndergaard, Zack Wheeler, and Steven Matz are just injuries waiting to happen. Even Jacob deGrom, who has been I believe the best starter this season, has a history of arm injuries that makes Mets front-office personnel nervous. Even Robert Gsellman and Seth Lugo are recovering from injuries sustained during this season. The bullpen has been just as bad. The bullpen so far has logged 257 innings to the tune of a 4.97 ERA. Not to mention they have not had a reliable closer since Jeurys Familia has been both suspended and injured this season, and the rest of the bullpen outside of Addison Reed and Jerry Blevins has been downright horrendous.

Overall, the Mets need to begin the next phase of the rebuilding process. With aging veterans and current players underperforming, it’s clear that the time for a championship has come and gone for this group. The Mets need to get younger and it starts with the old addition-by-subtraction technique. By dumping aging veterans with big contracts, the Mets will be able to allocate their resources and maybe pick up some pieces in free agency while simultaneously giving their top prospects playing time and allowing them to develop. As the great Cosmo Kramer once said on Seinfeld, “I think it’s time that we shut down and re-tool.”

Michael Pineda is having by far the best season of his career ever since he broke into the big leagues with Seattle in 2011. This is good news for Pineda who is in a contract year and looking to earn a huge payday on the open market this winter. However, this is bad news for teams, especially the Yankees, who have many questions surrounding their starting rotation with CC Sabathia also in a contract year and Masahiro Tanaka having the chance to opt out of his current contract after the season (although the latter seems unlikely at the moment). Pineda reminds me of one player in particular: former Yankee Ivan Nova.

Like Pineda, Nova has a fastball in the mid-90s and good secondary pitches, including a nasty curve and a change-up which he has begun to develop under Pittsburgh Pirates pitching coach Ray Searage, aka “the pitcher whisperer”. While Nova’s strikeout numbers have gone down, he has learned to pitch rather than just throw, which has resulted in fewer guys getting on base against him as well as his K/BB ratio going down, which I believe have been key contributing factors to his success in Pittsburgh. Also like Pineda, Nova hit the ground running, going 16-4 with a 3.70 ERA in 2011, and he was arguably the Yankees’ second-best starter behind Sabathia. However, as teams began to expose tendencies, combined with mounting injuries, Nova was never able to maintain the same level of success in New York.

The same could be said for Pineda, who missed two full seasons and most of 2014. Even after coming back in 2015, Pineda still struggled to maintain any level of consistency, after posting respectable numbers as a rookie. Now, Pineda has harnessed the power of his wipe-out slider and has become a ground ball pitcher (51.5%) to cope with the home-run haven that is Yankee Stadium. His K/BB ratio has gone down and his WHIP has dropped from 1.35 to 1.13 this season. The formula is simple: the fewer baserunners there are, the better a team’s chances are of winning. Also, like Nova, Pineda is using a change-up more in his pitching repertoire, to complement his slider. As a result, he has generated a 43.3% swing and miss percentage on pitches outside the zone, a 7% increase from last season. Additionally, they are close in age, since Nova was 30 when he signed his new contract, and Pineda will be 29.

The Pirates ended up giving Nova a three-year, $26-million contract last offseason. As long as Pineda continues to have success this season, he will also end up getting a similar deal. I predict he will end up staying with the Yankees for three years for somewhere in the range of$36-39 million simply because the Yankees will be desperate for starting pitching and may even pay a little bit over his market value to keep him. These types of deals are always risky, and many look to the Dodgers signing Rich Hill. However, Pineda has proven that he has always had the talent to pitch in New York and it seems that he finally has his head in the right place to help him reach his full potential. I believe that the Yankees will also re-sign Sabathia to a one-year deal in the range of $5-10 million, considering he will be 37 next season. If the Yankees manage to acquire another lefty or even sign Jake Arrieta, the Yankees starting rotation could be something to look out for in 2018.

Confession: fantasy baseball is life.

Second confession: the chance that I actually turn out to be a sabermetrician is <1%.

That being said, driven purely by competition and a need to have a leg up on the established vets in a 20-team, hyper-deep fantasy league, I had an idea to see if I could build a set of formulas that attempted to quantify a pitcher’s “true-talent level” by the performance of each pitch in his arsenal. Along with one of my buddies in the league who happens to be (much) better at numbers than yours truly, dSCORE was born.

dSCORE (“Dominance Score”) is designed as a luck-independent analysis (similar to FIP) — showing a pitcher might be overperforming/underperforming based on the quality of the pitches he throws. It analyzes each pitch at a pitcher’s disposal using outcome metrics (K-BB%, Hard/Soft%, contact metrics, swinging strikes, weighted pitch values), with each metric weighted by importance to success. For relievers, missing bats, limiting hard contact, and one to two premium pitches are better indicators of success; starting pitchers with a better overall arsenal plus contact and baserunner management tend to have more success. We designed dSCORE as a way to make early identification of possible high-leverage relievers or closers, as well as stripping out as much luck as possible to view a pitcher from as pure a talent point of view as possible.

We’ve finalized our evaluations of MLB relievers, so I’ll be going over those below. I’ll post our findings on starting pitchers as soon as we finish up that part — but you’ll be able to see the work in process in this Google Sheets link that also shows the finalized rankings for relievers.

Any reliever list that’s headed up by Chapman and Miller should be on the right track. Danny Duffy shows up, even though he spent most of the summer in the starting rotation. I guess that shows just how good he was even in a starting role!

We had built the alpha version of this algorithm right as guys like Edwin Diaz and Carl Edwards Jr. were starting to get national helium as breakout talents. Even in our alpha version, they made the top 10, which was about as much of a proof-of-concept as could be asked for. Other possible impact guys identified include Grant Dayton (#14), Matt Bush (#19), Josh Smoker (#26), Dario Alvarez (#28), Michael Feliz (#29) and Pedro Baez (#30).

Since I led with the results, here’s how we got them. For relievers, we took these stats:

Set 1: K-BB%

Set 2: Hard%, Soft%

Set 3: Contact%, O-Contact%, Z-Contact%, SwStk%

Set 4: vPitch,

Set 5: wPitch Set 6: Pitch-X and Pitch-Z (where “Pitch” includes FA, FT, SL, CU, CH, FS for all of the above)

…and threw them in a weighting blender. I’ve already touched on the fact that relievers operate on a different set of ideal success indicators than starters, so for relievers we resolved on weights of 25% for Set 1, 10% for Set 2, 25% for Set 3, 10% for Set 4, 20% for set 5 and 10% for Set 6. Sum up the final weighted values, and you get each pitcher’s dSCORE. Before we weighted each arsenal, though, we compared each metric to the league mean, and gave it a numerical value based on how it stacked up to that mean. The higher the value, the better that pitch performed.

What the algorithm rolls out is an interesting, somewhat top-heavy curve that would be nice to paste in here if I could get media to upload, but I seem to be rather poor at life, so that didn’t happen — BUT it’s on the Sum tab in the link above. Adjusting the weightings obviously skews the results and therefore introduces a touch of bias, but it also has some interesting side effects when searching for players that are heavily affected by certain outcomes (e.g. someone that misses bats but the rest of the package is iffy). One last oddity/weakness we noticed was that pitchers with multiple plus-to-elite pitches got a boost in our rating system. The reason that could be an issue is guys like Kenley Jansen, who rely on a single dominant pitch, can get buried more than they deserve.

I’ve watched many a baseball game over my lifetime (that’s 50+ years), and I’ve cringed every time I see a National League manager send his starting pitcher up to bat any time prior to the seventh inning. Especially with runners on base! Doesn’t he know that pitchers can’t hit? Doesn’t he know that if he would just pinch-hit for the lame-batting starter he’d improve his team’s chances of winning?

So, after years of pondering this problem for five seconds at a time every couple of days, I decided to see if I could build a solid quantitative case for never letting a pitcher come to the plate for a National League team (obviously this is not an issue for the American League with their designated hitters). How would this change the look of the team’s pitching staff? And more importantly, how many more games would a team expect to win in a season if they adopted a “pitchers never bat” strategy?

The answer to the first question is pretty easy. The staff would “look” different. There were would be no more “starting pitchers.” A team’s pitching staff would consist only of “relievers.” Sure, one of the “relievers” would throw the first pitch of the game and could technically be called a “starter,” but given that he’ll be taken out of the game as soon as his spot in the batting line-up comes up, he’s effectively a “reliever,” just like the other 10 or 11 guys on the staff.

Now, the conventional wisdom would say that the current starting pitchers, especially the “aces,” get in a groove, and can give you six or seven solid innings. Why would anyone take them out the game in the second or third inning? Well, let’s do a “cost-benefit” analysis and see if we can make a case for “The Pitchers Never Bat” strategy.

Key Components of the Case:

The two primary components of the analysis are 1) how many more runs would a team expect to score in a season by pinch-hitting for every pitcher, and 2) how many more runs would a team expect to give up in a season because their starting pitchers are no longer going six, seven, or more innings in an outing? Or, maybe the team adopting such a strategy would actually give up FEWER runs per year by giving up on the century-old strategy of planning for the starting pitcher to pitch deep into the game.

A third component of the analysis could include the benefit of being able to choose from any of the team’s entire staff (probably 11 or 12 pitchers) and use only the ones that look like they’ve got their “stuff” while warming up before the game, instead of sticking with the “starter” who is scheduled to pitch today because it’s his turn in the “rotation.”

A fourth component of the analysis could include the benefit a team could achieve because the other team can no longer stack their starting batting order with a lot of lefties (to face a right-handed starter), or with lot of righties (to face a left-handed starter), because the team with no “starters” will pinch-hit for their first pitcher after one, two, or three innings. So, in total, the “handedness battle” tilts slightly more in favor of the team implementing the new strategy.

A fifth component could include the cost (or benefit) of reducing the size of the pitching staff by one or two, and adding one or two more everyday players, who would be needed to pinch-hit in the early innings.

A sixth component could be an added benefit that batters will not be able to get “used to” a pitcher by seeing them multiple times in a single game. Under the new strategy batters will see each pitcher once, or, at most, twice in a game.

I’m going to focus on the two primary components above, and let the lessor components alone for now. Perhaps others can weigh in on how to quantify the potential impacts of these changes.

Component #1: How much more offense will the “Pitchers Never Bat” strategy create?

This is the easiest of the components to quantify. I will use the wOBA (weighted On Base Average) statistic as defined and measured by FanGraphs to evaluate this component. Let’s start with some basic information and rules-of-thumb.

Using data from the National League for the 2015 season I find that pinch-hitters have a wOBA of .275 across the entire league, while pitchers, when batting, had a wOBA of just .148 across the entire league. The difference in wOBA between pinch-hitters and pitchers is .127 (that’s .275 minus .148.) Note that all position players in the NL combined for an average wOBA of .318 in 2015. I’m assuming that our new pinch-hitters won’t get anywhere near that figure, but will be comparable to the 2015 pinch-hitters, who came in way lower, at .275.

Now, let’s assume we can replace every pitcher’s plate appearance (PA) with a pinch-hitter. This improvement of .127 in wOBA needs to be applied 336 times per season, because that was the average number of times that a National League team sent their pitchers up to the plate in 2015. And lastly, we need to know two rules of thumb from FanGraphs that are needed to complete the analysis of the first component: 1) every additional 20 points in wOBA is expected to result in an additional 10 runs per 600 plate appearances, and 2) every 10 additional runs a team expects to score in season translates into one additional win per year. OK – so, let’s do the math:

If 20 additional points of wOBA translates into 10 runs per 600 PA, then our new pinch-hitters who are now batting for pitchers will provide the team with 63.5 incremental runs per 600 PA (which equals 127/20 * 10.) And since these pinch-hitters will be coming to the plate 336 times, not 600 times, we need to reduce the 63.5 incremental runs per season down to 35.6 incremental runs per season (which is 336 / 600 * 63.5).

Finally, the last step is to take our 35.6 incremental runs per season and translate that into incremental wins per year using the rule-of-thumb that ten runs equates to one win. Therefore, our 35.6 extra runs results in an expected 3.6 incremental wins per year. That’s a decent-sized pick-up in expected wins.

OK, so now, what about the pitching staff? Will replacing the conventional pitching staff with a staff consisting of no starters and all relievers cause the runs allowed to increase, and if so, by how much? Enough to offset our 3.6 extra wins that we just picked up on offense?

Component #2: How many more runs will pitchers give up using the “Pitchers Never Bat” strategy?

Imagine, for the moment, that a GM is to build his pitching staff from scratch. (We’ll worry about how to transition from a conventional staff to an all-reliever staff later.) And let’s just assume he’ll pick just 11 pitchers. (Most NL teams use 12-man staffs while some use 13, so that will give the team one or two additional position players.) Currently, starting pitchers typically throw 160-200 innings per season, and relievers tend to throw 50-80 innings per season. But with the new all-reliever strategy, and using only 11 pitchers, each of our new guys will need to average around 130 innings each, with perhaps some pitching as much as 160, and some as low as 100 innings per year. So, the GM is looking for 11 guys who can each contribute 100-160 innings per season. Each outing will be for about one to three innings for each pitcher. How will they fare?

Let’s look at the National League’s pitchers for 2015. Starting pitchers had an aggregate WHIP (Walks Plus Hits per Inning Pitched) of 1.299, while relievers, in total, recorded an identical WHIP of 1.299. So my takeaway from this is that the average starter was equally as good (or bad) as the average reliever. From this, I am going to take a leap of faith, and assume that a staff of 11 new-style relievers could be expected to perform equivalently. (And that doesn’t even factor in some of the lesser elements of the new strategy, as mentioned above, such as Components 3 and 4 of the analysis.)

From this, albeit simplified, evaluation of Component #2, I estimate that a team moving to an all-reliever pitching staff will have an expected change in Runs Allowed of zero, and therefore the change will neither offset, nor supplement, the offensive benefit evaluated in Component #1.

Conclusion and Final Thoughts

In summary, using the two primary components of my analysis, I estimate that adopting a “Pitchers Never Bat” strategy in the National League (a.k.a. an “All Reliever Pitching Staff” strategy) will improve a team’s offense by an expected 36 runs per year, which will increase the team’s expected win total by 3.6 games. I estimate that the impact on runs allowed will be near zero. Some lesser elements, Components #3 through #6, could also add some additional value to the strategy.

Implementing the strategy does not necessarily need to be a complete, 100% adoption of the “pitchers never bat” rule. Modifications can be made. Perhaps a pitcher is doing well through two innings and comes to bat with two out and no one on base. In this case the manager could let the pitcher bat, so that he can stay in and pitch another two or three innings. This would change the name of the strategy to something like the “Pitchers Very, Very Rarely Bat” strategy.

As far as transitioning to an all-reliever staff from a conventional staff, it could be done over time, or only in part, such that a team could maintain, say, its two top aces, and complement them with eight or nine relievers. This way, the aces could pitch as they do now, going six-plus innings, every fifth day, while limiting the “Pitchers Never Bat” strategy to the three out of the five days when the two starters are resting.

Finally, let’s try to put a dollar value on this new strategy. The guys at FanGraphs, and other places, have tried to estimate how much teams are willing to pay for each additional win. Without going into all the various estimates and approaches at trying to answer that question, let’s just go with a simple $8 million per win. I’m sure it could be argued to be more or less, but let’s just put $8 million out there as a base case. If that’s true, a 3.6-win strategy, such as the “Pitchers Never Bat” strategy, is worth about $29 million per year. Go ahead and implement the strategy now, and, if it takes, say, three years before any of the other NL teams catch on, you’ve just picked up a cool $87 million (3 * 29 million).

And if the other components of the analysis (#3 through #6) are quantified and it can be determined that they add another 0.5 wins per year, which I think is quite doable, then we can get the total up to 4.1 wins per year, for a value of $33 million per year, or just around a cool $100 million over the first three years. And that’s how you make $100 million without really trying!